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	A corpus-based analysis on code-mixing features in Mandarin-English bilingual children in Singapore
	Huang, X.;  Zhang, C
	2018
会议日期	2018
会议地点	Tokyo, Japan
英文摘要	This paper investigated the code-mixing features in Mandarin-English bilingual children in Singapore. First, it examined whether the code-mixing rate was different in Mandarin Chinese and English contexts. Second, it explored the syntactic categories of code-mixing in Singapore bilingual children. Moreover, this study investigated whether morphological information was preserved when inserting syntactic components into the matrix language. Data are derived from the Singapore Bilingual Corpus, in which the recordings and transcriptions of sixty English-Mandarin 5-to-6-year-old children were preserved for analysis. Results indicated that the rate of code-mixing was asymmetrical in the two language contexts, with the rate being significantly higher in the Mandarin context than that in the English context. The asymmetry is related to language dominance in that children are more likely to code-mix when using their nondominant language. Concerning the syntactic categories of code-mixing words in the Singaporean bilingual children, we found that noun-mixing, verb-mixing, and adjective-mixing are the three most frequently used categories in code-mixing in the Mandarin context. This pattern mirrors the syntactic categories of code-mixing in the Cantonese context in Cantonese-English bilingual children, and the general trend observed in lexical borrowing. Third, our results also indicated that English vocabularies that carry morphological information are embedded in bare forms in the Mandarin context. These findings shed light upon how bilingual children take advantage of the two languages in mixed utterances in a bilingual environment.
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13727]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Huang, X., Zhang, C. A corpus-based analysis on code-mixing features in Mandarin-English bilingual children in Singapore[C]. 见:. Tokyo, Japan. 2018.

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